Actual Route Research Articles

Manual route modification using an oblique method following automatic virtual bronchoscopic navigation.

Virtual automatic bronchoscopic navigation (VBN) systems to determine the route to peripheral pulmonary lesions (PPLs) in lung cancer can improve diagnostic biopsy yields. However, compared with VBN, drawing manual routes using computed tomography images, especially with oblique methods, can identify more routes. The Ziostation2 VBN system combines the benefits of these 2 methods; we evaluated this performance by comparing 3 different route-determining methods.We retrospectively collected data from 50 patients with PPLs measuring <30 mm who underwent transbronchial biopsy with an ultrathin bronchoscope at the Osaka International Cancer Institute during January to December 2018. We compared automatic VBN (Ziostation2), manual route modification using an oblique method after automatic VBN, and manual navigation using a general application computed tomography viewer. Concordance between predicted and actual branching were determined. We also compared the predicted relationship between the terminal bronchi and the lesion by 2 of the methods with ultrasonographic images (radial-probe endobronchial ultrasonography [radial-EBUS]).Manual modification after automatic VBN significantly increased the rate of determining routes to the target (66%) versus with the automatic VBN alone (32%) (P < .001). Expected route bifurcations were exact matches with actual branching in 45/48 of the patients using manual modification after automatic VBN. The predicted relationship between the terminal bronchi and the lesion using manual modification after VBN matched the radial-EBUS images in 35/50 of the patients.Manual modification of routes to PPLs using an oblique method after automatic VBN predicted actual radial-EBUS route imaging and could help determine appropriate patients for bronchoscopy.

Determination of Distribution Routes Using the Saving Matrix Method to Minimize Shipping Costs at PT. SUKUN TRANSPORT LOGISTICS

PT. Sukun Transport Logistics is a company engaged in shipping,logistics and also transportation vehicle rental services. Theproblems faced by PT. Sukun Transport Logistics is a lack oftransport vehicle capacity, causing the use of transport vehiclesthat are not suitable and the distribution of sub-routes is notbalanced. Therefore, to overcome these problems, it is necessaryto determine the optimal distribution route using the saving matrixmethod. The saving matrix method is a method to shortenmileage, estimate distribution time and minimize shipping costs atPT. Sukun Transport Logistics. From the results of the routeresearch in this study, it is known that the route proposal with thesaving matrix method opens 2 sub routes from the previous 3 subroutes, the total distance through the saving matrix method andthe closest calculation method is 299 km. While the total distanceof the actual route delivery to the company is 313 km. Thedistance saving of this research with the company's actual routeis 14 km or 4.47%. At the time of saving of 164 minutes or20.97%, and distribution cost savings of Rp. 141,658 or 25.53%.

Design of Cyber-Physical Waste Management Systems Focusing on Energy Efficiency and Sustainability

Industry 4.0 tools, such as the Internet of Things, artificial intelligence, digital twinning, and cloud computing, create a technological revolution that accelerates efforts to optimize the efficiency of cyber-physical operations and services. The waste management system requires a critical share of city logistics optimization, especially when using cyber-physical systems. Modern tools reduce the required municipal waste handling, such as loading and unloading, transportation, and warehousing, which leads to an increase in efficiency and flexibility, saving energy and time, and protecting the environment. In this paper, we present a cyber-physical waste management system solution by providing a cyber-physical model design and description, mathematical modeling, and two cases to investigate the impact on energy consumption and emissions. After an introduction and literature review, we describe the design of the cyber-physical model and tackle the first echelon. The designed system incorporates the IoT, smart bins with multi-percentage sensors, data and information analysis, vehicles’ actual routes, energy and emissions optimization, multi-echelon systems, time windows, and flexibility. Mathematical modeling equations for the optimized total energy consumption are presented. Thirty and twenty smart bins located in VIII District in Budapest are detailed as two case studies, where solutions for the optimized real routes and energy consumption are found using three metaheuristic algorithms: genetic, particle swarm, and simulated annealing optimization algorithms. The accrued emissions of CO, NMHC, CH4, NOx, and PM for the optimized solutions are calculated. Finally, the results are compared with a random traditional solution to measure the effectiveness.

Development of Bus Routes Reorganization Support Software Using the Naïve Bayes Classification Method

Reorganizing city bus routes is generally accomplished by designing bus supply methods to meet passenger demand. The bus supply method involves establishing bus routes and planning their schedules. The actual bus route reorganization decisions are not determined simply by balancing passenger demand and bus supply, but are based on other complex interests, such as bus routes that must exist for welfare but where profits are low. Machine learned prediction models could be helpful when considering such factors in the decision-making process. Here, the Naïve Bayes algorithm was applied to develop the classifier model because of its applicability, even with a limited amount of training data. As the input characteristics for the Naïve Bayes algorithm, data for each individual bus route were featured and cleansed with the actual route improvement decisions. A number of classification models were created by changing training sets and then compared in terms of classification performance such as accuracy, precision, and recall. Modeling and tests were conducted to show how Naïve Bayes classifiers learned in the form of supervised learning can help the route reorganization work. Results from a local governments’ actual route reorganization study were used to train and test the proposed machine learning classification model. As the main contribution of this study, a prediction model was developed to support shortening decision-making for each route, using machine learning algorithms and actual route reorganization research case data. Results verified that such an automatic classifier, or initial route decision proposal software, can provide intuitive support in actual route reorganization research.

Hierarchical Planning for Heterogeneous Multi-Robot Routing Problems via Learned Subteam Performance

This letter considersa particular class of multi-robot task allocation problems, where tasks correspond to heterogeneous multi-robot routing problems defined on different areas of a given environment. We present a hierarchical planner that breaks down the complexity of this problem into two subproblems: the high-level problem of allocating robots to routing tasks, and the low-level problem of computing the actual routing paths for each subteam. The planner uses a Graph Neural Network (GNN) as a heuristic to estimate subteam performance for specific coalitions on specific routing tasks. It then iteratively refines the estimates to the real subteam performances as solutions of the low-level problems become availableon a testbed problem having a heterogeneous multi-robot area inspection problem as the base routing task, we empirically show that our hierarchical planner is able to compute optimal or near-optimal (within 7%) solutions approximately 16 times faster (on average) than an optimal baseline that computes plans for all the possible allocations in advance to obtain precise routing times. Furthermore, we show that a GNN-based estimator can provide an excellent trade-off between solution quality and computation time compared to other baseline (non-learned) estimators.

Impact of the Vehicle Environment on the Thermal Behavior of the Electrical Wiring

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The thermal behavior of wires within the electrical distribution system (EDS) has a strong impact on the conductor cross section, the type of insulation, the derating, and the fusing system, and therefore on weight, cost, and reliability. Consequently, significant efforts have been made to develop sound static and dynamic thermal models for single wires and wire bundles. However, these models are based on the simplifying assumption that the object is completely surrounded by air, where, with the exception of free convection, airflow can be neglected, and where no interaction with other objects is considered.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;The approach presented in this paper takes into account the actual environment and routing within a vehicle, where some objects such as metal sheets can be considered as heat sinks and other objects, e.g. a motor block, as heat sources. For this reason, measurements were performed using an experimental set-up that allows any desired positioning and alignment of the DuT (device under test, here: wire, cable, bundle) and of two temperature-controlled heat source/sink objects. These objects are a large rectangular surface and a small sphere, in order to be able to emulate as many spatial configurations as possible. The overall set-up is protected against externally-induced airflows. Because the exact modelling of the heat transfer processes (radiation, conduction, and free/forced convection) would require time-consuming FEM calculations for the investigated complex geometries, a simplified and partially data-driven model is proposed. The paper describes the theory and the modeling approach, and presents initial simulation and measurement results.&lt;/div&gt;&lt;/div&gt;

Collaborative Optimization of Vehicle and Crew Scheduling for a Mixed Fleet with Electric and Conventional Buses

Replacing conventional buses with electric buses is in line with the concept of sustainable development. However, electric buses have the disadvantages of short driving range and high purchase price. Many cities must implement a semi-electrification strategy for bus routes. In this paper, a bi-level, multi-objective programming model is established for the collaborative scheduling problem of vehicles and drivers on a bus route served by the mixed bus fleet. The upper-layer model minimizes the operation cost and economic cost of carbon emission to optimize the vehicle and charging scheme; while the lower-layer model tries to optimize the crew-scheduling scheme with the objective of minimizing driver wages and maximizing the degree of bus-driver specificity, considering the impact of drivers’ labor restrictions. Then, the improved multi-objective particle swarm algorithm based on an ε-constraint processing mechanism is used to solve the problem. Finally, an actual bus route is taken as an example to verify the effectiveness of the model. The results show that the established model can reduce the impact of unbalanced vehicle scheduling in mixed fleets on crew scheduling, ensure the degree of driver–bus specificity to standardize operation, and save the operation cost and driver wage.

Matching algorithm for compact ride‐sharing in rural area using genetic algorithm

AbstractTo solve the last mile problem in rural areas, we deal with compact ride‐sharing model. For reducing the computational cost, we use a simple GA method and compared the performance of the parameters. To simplify the problem, our algorithm use a simple linear distance and minimize the total traveling distance. A data set used for GA was based on the Inami area in Nanto City, Toyama Prefecture, that is a real case study region. The performance comparison experiment of the algorithm by changing parameters was carried out. Experimental results show that the algorithm is likely to give correct answers for up to four vehicles. It was also shown that the combination of population size and elite proportion for GA could reduce computational costs while ensuring accuracy. In addition, by ride‐sharing was actually carried out using the obtained experimental result, it was shown that it could be applied to the actual route even if the calculation was carried out in the linear distance.

Matching Algorithm for Compact Ride-sharing in Rural Area using Genetic Algorithm

To solve the last mile problem in rural areas, we deal with compact ride-sharing model. For reducing the computational cost, we use a simple GA method and compared the performance of the parameters. To simplify the problem, our algorithm use a simple linear distance and minimize the total traveling distance. A data set used for GA was based on the Inami area in Nanto City, Toyama Prefecture, that is a real case study region. The performance comparison experiment of the algorithm by changing parameters was carried out. Experimental results show that the algorithm is likely to give correct answers for up to four vehicles. It was also shown that the combination of population size and elite proportion for GA could reduce computational costs while ensuring accuracy. In addition, by ride-sharing was actually carried out using the obtained experimental result, it was shown that it could be applied to the actual route even if the calculation was carried out in the linear distance.

SASA: Source-Aware Self-Attention for IP Hijack Detection

IP hijack attacks deflect traffic between endpoints through the attacker network, leading to man-in-the-middle attacks. Current detection solutions are only based on AS-level path analysis, while attacks that include data-plane manipulations may exhibit only geographic anomalies and preserve the AS-level route, or hide the problematic AS in the path. Thus, there is a need to develop data-plane analysis frameworks that examine the actual route packets traverse. We introduce here a deep learning system that examines the geography of traceroute measurements to detect malicious routes. We use multiple geolocation services, with various levels of confidence; each also suffers from location errors. Moreover, identifying a hijacked route is not sufficient since an operator presented with a hijack alert needs an indication of the cause for flagging out the problematic route. Thus, we introduce a novel deep learning layer, called Source-Aware Self-Attention ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SASA</i> ), which is an extension of the attention mechanism. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SASA</i> learns each data source’s confidence and combines this score with the attention of each router in the route to point out the most problematic one. We validate our IP hijacking classification method using two router data types: coordinates and country location, and show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SASA</i> outperforms the regular self-attention layer, using the same neural network architecture, and achieves extremely high accuracy.

Estimating Congestion in a Fixed-Route Bus by Using BLE Signals.

Information on congestion of buses, which are one of the major public transportation modes, can be very useful in light of the current COVID-19 pandemic. Because it is unrealistic to manually monitor the number of riders on all buses in operation, a system that can automatically monitor congestion is necessary. The main goal of this paper’s work is to automatically estimate the congestion level on a bus route with acceptable performance. For practical operation, it is necessary to design a system that does not infringe on the privacy of passengers and ensures the safety of passengers and the installation sites. In this paper, we propose a congestion estimation system that protects passengers’ privacy and reduces the installation cost by using Bluetooth low-energy (BLE) signals as sensing data. The proposed system consists of (1) a sensing mechanism that acquires BLE signals emitted from passengers’ mobile terminals in the bus and (2) a mechanism that estimates the degree of congestion in the bus from the data obtained by the sensing mechanism. To evaluate the effectiveness of the proposed system, we conducted a data collection experiment on an actual bus route in cooperation with Nara Kotsu Co., Ltd. The results showed that the proposed system could estimate the number of passengers with a mean absolute error of 2.49 passengers (error rate of 38.8%).

ABM RoutePlanner: An agent-based model simulation for suggesting preference-based routes in Spain

ABSTRACT Spain ranks second in the world for the number of international tourists. These tourists have different preferences, which influence their choice of tourist routes depending on the activities offered by provinces. There are currently no routes customised according to the preferences of a travel party, which makes the supply of tourist packages complex. We propose an agent-based model, named ABM RoutePlanner, which simulates the behaviour of travel parties through provinces of Spain. The model is developed as an application of the ODD protocol. This paper makes two contributions. First, we describe a model applicable to the identification of appropriate routes for different combinations of tourists’ preferences in some (hypothetical) environments. Second, we present the actual routes that will allow tour operators to define strategic plans that motivate tourists to visit the provinces included in the routes. The simulation model was calibrated with data extracted from TripAdvisor and Spanish tourist surveys.

Roald Amundsen’s route across the polar plateau in 1911–1912

Abstract Roald Amundsen’s exact route from the top of the Axel Heiberg glacier to the South Pole and back in 1911–1912 has always been somewhat unclear because he never observed his longitude during his southern journey. His approach was simply to steer approximately in a true southerly direction by magnetic compass as long as obstacles did not force him to deviate. The fact that he only knew approximately where he was most of the time on the polar plateau never caused any severe problems for him, but it complicated the search for a depot during the return journey. Based on Amundsen’s bearings of some peaks in the Transantarctic Mountains, in combination with his compass courses adjusted with accurate values for the magnetic declination at the time, this paper elucidates Amundsen’s actual route across the polar plateau in 1911–1912. The main result is that Amundsen must have taken a more easterly route than what previously has been assumed.

Analysis of Route Choice During Planned and Unplanned Road Closures

The Federal Highway Administration (FHWA) Alternate Route Handbook proposes guidance to identify alternate routes during planned and unplanned road closures. A challenge with this process is the lack of traffic data available to decision-makers. High volume corridors experiencing unplanned closures can provide a rich case history by systematically collecting connected vehicle (CV) data during such incidents. CV data provide the ability to directly measure actual diversion routes and travel times during an ongoing or historical incident. This paper presents methodologies to systematically analyze diversion data to identify the most common alternate route choices and impacted interstate exits, valuable information for public safety and transportation agencies to evaluate the surrounding road network&#x2019;s resiliency in accommodating diverting traffic. Agencies can use this information to proactively deploy resources (officers, signs, barricades) at critical locations during future closures. The scalability of this methodology is demonstrated by evaluating 12 additional cases to assess diversion rates found to be in the range of 58% to 93% for total closures exceeding five hours. The paper concludes by recommending agencies apply these methodologies to develop data-driven diversion strategies on critical routes coupled with real-time CV monitoring in dispatch centers to provide agile adjustment of resources along diversion routes.

Desired versus actual delivery route: nursing students' perception about their type of delivery.

To identify the perception of nursing student mothers regarding the desired type of delivery in relation to the one performed. Qualitative research, supported by the assumptions of Humanization of Obstetric Care, developed remotely with 16 nursing students who experienced the parturition process. The data were collected through an online focus group using the Google Meet program, guided by a semi-structured script. The speeches were analyzed using the Thematic Content Analysis technique. An unsatisfactory discrepancy was identified between the mother's preference and the realization, predominantly the desire for the vaginal way due to academic knowledge about the benefits. However, the cesarean section was predominant as a procedure performed due to possible complications, fears, financial situation of the parturient woman and control of choice by the health professional. Perception indicates marked disagreements, with the presence of complications, fear, lack of autonomy and predominance of a hegemonic care model, reinforcing the need for expansion and application of the Humanization of Obstetric Care, as a primary condition for proper monitoring.

Temporal and Spatial Analysis of Actual Transfer Route of Bus Passengers - A Case Study on &quot;Kururin-bus&quot; in Nisshin City, Japan

Temporal and Spatial Analysis of Actual Transfer Route of Bus Passengers - A Case Study on &quot;Kururin-bus&quot; in Nisshin City, Japan

Using an ant algorithm to find a strategy for the behavior of a group of mobile robots on a work field with obstacles

A more complex solution to the problem of the distribution of tasks in a group of mobile robots, in the presence of obstacles in the workspace, is considered. The work is a continuation of a cycle of research in which the basic algorithm for solving the tasks was one of the types of ant algorithm — the multicolonial ant system method in combination with the trajectory planning algorithm implemented using the principle of dynamic programming. The task statement, the workspace model, the goals of the robots functioning and the parameters characterizing their work have been adjusted. The choice of free parameters of the ant algorithm for performing multi-criteria optimization and tuning of the solution is carried out: the number of iterations, the number of intercolonial groups of ants, the weight of the concentration of the pheromone of arcs, the weight of the heuristic attractiveness of arcs and the pheromone evaporation coefficient. The results of computational experiments conducted in the presence of static (known in advance) and dynamic (other robots) obstacles in the workspace are presented. The proposed algorithm was tested using the example of a group consisting of three robots performing 10 tasks. As shown in the results of computational experiments, robot trajectories are built on a subset of free cells of the workspace and do not intersect cells with obstacles. At the same time, the configuration of the work field affects not only the actual routes of robots, but also the redistribution of tasks between them, and the number of robots involved. Additionally, a series of computational experiments with different combinations of values of free parameters was carried out to determine the optimal ratios and implement a more efficient ant algorithm. Optimization was carried out by a single adjustment method, which allowed us to find the required values of free parameters. It is shown that the adjustment of the parameters made it possible to reduce the relative error in the synthesis of the optimal route of movement of a group of robots by 3–6 %.

Accounting for the carbon emissions from domestic air routes in China

This study seeks to build a database of China's 413 main routes in 2018, which contains the carbon emission intensity of the leading aircraft types, the Climb/Cruise/Descent (CCD) carbon emissions of each airline and each route, and the probable emissions when the routes are straight. First, the Modified Fuel Percentage Method (MFPM) is applied to calculate the carbon emission intensity of main aircraft types at various distances. Next, the carbon emissions and the Revenue Passenger Kilometers of 413 routes and 40 airlines are calculated. Then the carbon emissions of the actual route and straight route are analyzed. Dali-Kunming and Joy Air have the most significant emission intensity among the routes and airlines. However, the excess emissions from the non-straight routes account for about 10.15% of the actual emissions.

Various Routing Protocols of Vehicular Ad hoc Network: A Survey

Abstract: A vehicular ad hoc network is a type of network divided by an area where car nodes can join or leave the network. Due to the flexibility of the network environment active contractual routes are used in the development of the route from the source to the destination. Various active and active protocols are updated in this paper. Active routing protocols are those that use current network information in the development of the route from the source to the destination. Active routing protocols are those that use the pre-defined network information in the route setting. Active route agreements use route tables in route development. In this review paper, a literature survey was conducted on VANET route protocols. It is analyzed that an effective route protocol offers higher performance compared to active router protocols. Keywords: VANET, Reactive, Proactive Routing

Data-Driven Performance Evaluation Framework for Multi-Modal Public Transport Systems.

Transport agencies require accurate and updated information about public transport systems for the optimal decision-making processes regarding design and operation. In addition to assessing topology and service components, users’ behaviors must be considered. To this end, a data-driven performance evaluation based on passengers’ actual routes is key. Automatic fare collection platforms provide meaningful smart card data (SCD), but these are incomplete when gathered by entry-only systems. To obtain origin–destination (OD) matrices, we must manage complete journeys. In this paper, we use an adapted trip chaining method to reconstruct incomplete multi-modal journeys by finding spatial similarities between the outbound and inbound routes of the same user. From this dataset, we develop a performance evaluation framework that provides novel metrics and visualization utilities. First, we generate a space-time characterization of the overall operation of transport networks. Second, we supply enhanced OD matrices showing mobility patterns between zones and average traversed distances, travel times, and operation speeds, which model the real efficacy of the public transport system. We applied this framework to the Comunidad de Madrid (Spain), using 4 months’ worth of real SCD, showing its potential to generate meaningful information about the performance of multi-modal public transport systems.